AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

This dataset, released February 2021, contains the statistics of premature mortality by various causes for people below 75 years, over the years 2014 to 2018. Causes for death include cancer (colorectal, lung, breast), diabetes, circulatory system diseases (ischaemic heart disease, cerebrovascular disease), respiratory system diseases (chronic obstructive pulmonary disease), and external causes (road traffic injuries, suicide and self-inflicted injuries) The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP) for Australia, 30 June 2014 to 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Lung Cancer Prediction

Air Pollution, Alcohol, Smoking & Risk of Lung Cancer

About this dataset

This dataset contains information on patients with lung cancer, including their age, gender, air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker, chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring

How to use the dataset

Lung cancer is the leading cause of cancer death worldwide, accounting for 1.59 million deaths in 2018. The majority of lung cancer cases are attributed to smoking, but exposure to air pollution is also a risk factor. A new study has found that air pollution may be linked to an increased risk of lung cancer, even in nonsmokers.

The study, which was published in the journal Nature Medicine, looked at data from over 462,000 people in China who were followed for an average of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution.

The researchers found that the people in the high-pollution group were more likely to develop lung cancer than those in the low-pollution group. They also found that the risk was higher in nonsmokers than smokers, and that the risk increased with age.

While this study does not prove that air pollution causes lung cancer, it does suggest that there may be a link between the two. More research is needed to confirm these findings and to determine what effect different types and levels of air pollution may have on lung cancer risk

Research Ideas

• predicting the likelihood of a patient developing lung cancer
• identifying risk factors for lung cancer
• determining the most effective treatment for a patient with lung cancer

Acknowledgements

License

See the dataset description for more information.

Columns

File: cancer patient data sets.csv | Column name | Description | |:-----------------------------|:--------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Gender | The gender of the patient. (Categorical) | | Air Pollution | The level of air pollution exposure of the patient. (Categorical) | | Alcohol use | The level of alcohol use of the patient. (Categorical) | | Dust Allergy | The level of dust allergy of the patient. (Categorical) | | OccuPational Hazards | The level of occupational hazards of the patient. (Categorical) | | Genetic Risk | The level of genetic risk of the patient. (Categorical) | | chronic Lung Disease | The level of chronic lung disease of the patient. (Categorical) | | Balanced Diet | The level of balanced diet of the patient. (Categorical) | | Obesity | The level of obesity of the patient. (Categorical) | | Smoking | The level of smoking of the patient. (Categorical) | | Passive Smoker | The level of passive smoker of the patient. (Categorical) | | Chest Pain | The level of chest pain of the patient. (Categorical) | | Coughing of Blood | The level of coughing of blood of the patient. (Categorical) | | Fatigue | The level of fatigue of the patient. (Categorical) | | Weight Loss | The level of weight loss of the patient. (Categorical) | | Shortness of Breath | The level of shortness of breath of the patient. (Categorical) | | Wheezing | The level of wheezing of the patient. (Categorical) | | Swallowing Difficulty | The level of swallowing difficulty of the patient. (Categorical) | | Clubbing of Finger Nails | The level of clubbing of finger nails of the patient. (Categorical) |

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

The role of religion and politics in the responses to the coronavirus pandemic raises the question of their influence on the risk of other diseases. This study focuses on age-adjusted death rates of cancer, heart disease, and infant mortality per 1000 live births before the pandemic (2018-2019) and COVID-19 in 2020-2021. Eight hypothesized predictors of health effects were analyzed by examining their correlation to age-adjusted death rates among U.S. states, percentage who pray once or more daily, Republican influence on state health policies as indicated by the percentage vote for Trump in 2016, percent of household incomes below poverty, median family income divided by a cost-of-living index, the Gini income inequality index, urban concentration of the population, physicians per capita, and public health expenditures per capita. Since prayer for divine intervention is common to otherwise diverse religious beliefs and practices, the percentage of people claiming to pray daily in each state was used to indicate potential religious influence. All of the death rates were higher in states where more people claimed to pray daily, and where Trump received a larger percentage of the vote. Except for COVID-19, the death rates were consistently lower in states with higher public health expenditures per capita. Only COVID-19 was correlated to physicians per capita, lower where there were more physicians. Corrected statistically for the other factors, income per cost of living explains no variance. Heart disease and COVID-19 death rates were higher in areas with more income inequality. All of the disease rates were in correlation with more rural populations. Correlation of daily prayer with smoking cigarettes, and neglect of public health recommendations for fruit and vegetable consumption and COVID-19 vaccination suggests that prayer may be substituted for preventive practices.

This table contains 4032 series, with data for years 1994/1998 - 2009/2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada; Inuit Nunangat; Inuvialuit Region; Nunavut; ...) Sex (3 items: Both sexes; Males; Females) Indicators (2 items: Mortality; Potential years of life lost) Selected causes of death (16 items: Total, all causes of death; All malignant neoplasms (cancers); Colorectal cancer; Lung cancer; ...) Characteristics (7 items: Number; Rate; Low 95% confidence interval, rate; High 95% confidence interval, rate; ...).

ObjectivesTo describe the procedures to derive complete prevalence and several indicators of cancer cure from population-based cancer registries.Materials and methodsCancer registry data (47% of the Italian population) were used to calculate limited duration prevalence for 62 cancer types by sex and registry. The incidence and survival models, needed to calculate the completeness index (R) and complete prevalence, were evaluated by likelihood ratio tests and by visual comparison. A sensitivity analysis was conducted to explore the effect on the complete prevalence of using different R indexes. Mixture cure models were used to estimate net survival (NS); life expectancy of fatal (LEF) cases; cure fraction (CF); time to cure (TTC); cure prevalence, prevalent patients who were not at risk of dying as a result of cancer; and already cured patients, those living longer than TTC at a specific point in time. CF was also compared with long-term NS since, for patients diagnosed after a certain age, CF (representing asymptotical values of NS) is reached far beyond the patient’s life expectancy.ResultsFor the most frequent cancer types, the Weibull survival model stratified by sex and age showed a very good fit with observed survival. For men diagnosed with any cancer type at age 65–74 years, CF was 41%, while the NS was 49% until age 100 and 50% until age 90. In women, similar differences emerged for patients with any cancer type or with breast cancer. Among patients alive in 2018 with colorectal cancer at age 55–64 years, 48% were already cured (had reached their specific TTC), while the cure prevalence (lifelong probability to be cured from cancer) was 89%. Cure prevalence became 97.5% (2.5% will die because of their neoplasm) for patients alive >5 years after diagnosis.ConclusionsThis study represents an addition to the current knowledge on the topic providing a detailed description of available indicators of prevalence and cancer cure, highlighting the links among them, and illustrating their interpretation. Indicators may be relevant for patients and clinical practice; they are unambiguously defined, measurable, and reproducible in different countries where population-based cancer registries are active.

PurposeBone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data.MethodsThis study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold.ResultsAmong all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807–0.833), 0.820 (95% CI: 0.807–0.833), and 0.815 (95% CI: 0.801–0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001).ConclusionsUsing machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

ObjectiveThe role of spousal support has been recognized to benefit patients with many chronic diseases and cancers. However, the impact of marital status on the survival of middle-aged and elderly patients with primary bone tumors remains elusive.Materials and methodsThe data of patients aged ≥ 45 years with primary bone tumors diagnosed between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results Database. Kaplan–Meier analysis was used to assess the overall survival and tumor-specific survival of patients. The Cox proportional hazards and Fine-and-Gray models were used to calculate the hazard ratios (HRs) and sub-distribution HRs (sHR) and the corresponding 95% confidence interval (CI) of all-cause mortality and tumor-specific mortality, respectively.ResultsA total of 5,640 primary bone tumors were included in the study. In 45–59 years cohort, married, unmarried, divorced and widowed accounted for 66.0, 21.0, 11.2, and 1.8%, respectively; while 64.3, 10.1, 8.8, and 16.8% in 60+ years cohort, respectively. The widowed patients had a lower proportion of early-stage tumors at diagnosis than that married, unmarried, and divorced patients (31.0% vs. 36% vs. 37.1% vs. 39.4%; P = 0.008), and had a higher proportion of patients who did not undergo surgery than that of married, unmarried, and divorced patients (38.6% vs. 21.3% vs. 24.6% vs. 24.4%; P < 0.001). The widowed population had an increased risk of all-cause mortality (HR, 1.68; 95% CI, 1.50–1.88; P < 0.001) and disease-related mortality (HR, 1.33; 95% CI, 1.09–1.61; P = 0.005) compared with the married population.ConclusionThe marital status of middle-aged and elderly people can affect the tumor stage at diagnosis, treatment, and survival prognosis of patients with primary bone cancer. Widowed patients are more inclined to choose non-surgical treatment and have the worst prognosis.

IntroductionIn patients with Common Variable Immunodeficiency, malignancy has been reported as the leading cause of death in adults, with a high risk of B-cell lymphomas and gastric cancer.MethodsWe conducted a five-year prospective study aiming to update the incidence and mortality of gastric cancer and the incidence of gastric precancerous lesions in 512 CVID patients who underwent a total of 400 upper gastrointestinal endoscopies.ResultsIn the pre-pandemic period, 0.58 endoscopies were performed per patient/year and in the COVID-19 period, 0.39 endoscopies were performed per patient/year. Histology revealed areas with precancerous lesions in about a third of patients. Patients who had more than one gastroscopy during the study period were more likely to have precancerous lesions. Two patients received a diagnosis of gastric cancer in the absence of Helicobacter pylori infection. The overall prevalence of Helicobacter pylori infection in biopsy specimens was 19.8% and related only to active gastritis. Among patients who had repeated gastroscopies, about 20% progressed to precancerous lesions, mostly independent of Helicobacter pylori.DiscussionWhile gastric cancer accounted for one in five deaths from CVID in our previous survey, no gastric cancer deaths were recorded in the past five years, likely consistent with the decline in stomach cancer mortality observed in the general population. However, during the COVID-19 pandemic, cancer screening has been delayed. Whether such a delay or true decline could be the reason for the lack of gastric cancer detection seen in CVID may become clear in the coming years. Due to the high incidence of precancerous lesions, we cannot rely on observed and predicted trends in gastric cancer mortality and strongly recommend tailored surveillance programs.

ObjectiveProstate cancer (PC) is the most common malignant tumour in men, and atherosclerotic cardiovascular disease (ASCVD) is the leading cause of non-cancer death in PC patients. The main purpose of this study was to investigate whether chemotherapy increases heart-specific mortality (HSM) in elderly patients with PC.MethodsPatient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018. We included all elderly patients with PC. The multivariate logistic regression model was used to explore the influencing factors of patients receiving chemotherapy. Confounders were excluded using a 1:1 proportional propensity score match, and a competing risk model and cumulative incidence plot were used to analyze HSM and other cause mortality (OCM) in patients who received chemotherapy versus those who did not.ResultsA total of 135183 elderly prostate patients were enrolled in this study, of whom 1361 received chemotherapy. The multivariate logistic regression model showed that older patients were more likely to not receive chemotherapy, married patients were more likely to receive chemotherapy, and the higher the TNM stage and tumor histological grade, the more patients received chemotherapy. In the original cohort before unmatched, there was no significant difference in HSM between chemotherapy and non-chemotherapy patients (P = 0.27). After 1:1 matching, HSM was significantly higher in patients without chemotherapy than in patients with chemotherapy (HR 2.54; P =0.002).ConclusionsOur results indicate that HSM is significantly higher in patients without chemotherapy than in those with chemotherapy. Therefore, although chemotherapy can lead to cardiotoxicity in elderly patients with PC, chemotherapy does not increase the HSM of patients and will benefit patients in the long-term survival.

BackgroundHepatitis C virus (HCV) infection affects men and women differently, yet few studies have investigated sex differences in long-term mortality risk among the HCV-infected population. We conducted a population-based study to elucidate all-cause and cause-specific mortality among men and women with HCV infection.MethodsThe study population consisted of adult participants from the 1999–2018 National Health and Nutrition Examination Survey, including 945 HCV-infected and 44,637 non-HCV-infected individuals. HCV infection was defined as either HCV seropositivity or detectable HCV RNA. Participants were followed until the date of death or December 31, 2019, to determine survival status.ResultsThe HCV-infected population, both male and female, tended to be older, more likely to be Black, single, have lower income, lower BMI, higher prevalence of hypertension, and were more likely to be current smokers. During a median follow-up of 125.0 months, a total of 5,309 participants died, including 1,253 deaths from cardiovascular disease (CVD) and 1,319 deaths from cancer. The crude analysis showed that the risk of death from all causes and from cancer, but not from CVD, was higher in the HCV-infected population. After adjusting for potential confounders, we found that both HCV-infected men (HR 1.41, 95% CI 1.10–1.81) and women (HR 2.03, 95% CI 1.36–3.02) were equally at increased risk of all-cause mortality compared to their non-HCV infected counterparts (p for interaction > 0.05). The risk of cancer-related mortality was significantly increased in HCV-infected women (HR 2.14, 95% CI 1.01–4.53), but not in men, compared to non-HCV-infected counterparts. Among HCV-infected population, there was no difference in the risks of all-cause, CVD-related, or cancer-related death between men and women.ConclusionBoth men and women with HCV infection had an increased risk of death from all causes compared to their non-HCV infected counterparts, but we did not observe a significant sex difference.

Risk of death from all causes, cardiovascular disease (CVD), and cancer in men compared to women (reference group), both for the total population and stratified by hepatitis C virus (HCV) infection status.